Bridging the Activity Gap: Why AI Struggles with Aging

Author: Denis Avetisyan

New research reveals the challenges of deploying machine learning models for human activity recognition when training data doesn’t reflect the behavioral patterns of older adults.

Behavioral models trained on data from younger individuals may falter when applied to older adults, as age-related shifts in behavioral distributions introduce unseen patterns and undermine the reliability of inferences made from training data.

This paper introduces HAROOD, a benchmark dataset and evaluation framework designed to assess out-of-distribution generalization in sensor-based human activity recognition, specifically addressing the impact of age-related behavioral differences.

Despite advances in sensor-based human activity recognition (HAR), performance often degrades when models encounter data differing from their training distribution-a critical limitation for real-world deployment. To address this, we introduce HAROOD: A Benchmark for Out-of-distribution Generalization in Sensor-based Human Activity Recognition, a comprehensive testbed encompassing six datasets, sixteen comparative methods, and four distinct distributional shift scenarios. Our extensive experiments reveal that no single out-of-distribution (OOD) generalization technique consistently outperforms others across these scenarios, highlighting substantial opportunities for algorithm development. Will a unified approach to OOD generalization emerge, or will specialized methods be required to tackle the diverse challenges within sensor-based HAR?

Predicting the Predictable: The Illusion of Insight

The capacity to foresee another’s actions hinges on discerning the repeatable structures within their behavior. This isn’t simply about cataloging what someone does, but identifying the consistent logic governing those choices. Researchers posit that individuals don’t operate randomly; instead, actions stem from internalized models of the world and goals, leading to predictable tendencies. By mapping these behavioral patterns – whether in gait, purchasing habits, or social interactions – it becomes possible to estimate the likelihood of future actions with increasing accuracy. This principle underpins everything from anticipating customer needs to forecasting public health crises, demonstrating that prediction isn’t clairvoyance, but rather a sophisticated application of pattern recognition applied to the study of human and animal behavior.

The ability to infer behavior – to understand the ‘why’ behind actions – is proving increasingly vital across a surprisingly broad spectrum of disciplines. In public health, behavioral inference helps model disease spread and design effective interventions, predicting how populations might respond to public health campaigns or emerging threats. Meanwhile, personalized assistance systems, from smart home devices to adaptive learning platforms, rely heavily on understanding user habits to anticipate needs and deliver tailored experiences. This extends to areas like financial forecasting, where predicting consumer spending patterns is paramount, and even robotics, where robots must interpret human intentions to collaborate safely and effectively. Ultimately, a robust capacity for behavioral inference promises to unlock more proactive and responsive solutions across countless facets of modern life, bridging the gap between observation and prediction.

The capacity to anticipate human action hinges on the quality of predictive models, and these models, in turn, are fundamentally limited by the data upon which they are built and their ability to capture the inherent complexity of behavior. Robust datasets, encompassing diverse scenarios and individual variations, are essential for training models that generalize beyond specific instances. However, even abundant data is insufficient if the model cannot represent the full spectrum of possible actions – human behavior isn’t simply a selection from a few likely choices, but a distribution across a potentially infinite range of possibilities. Consequently, advanced techniques are often employed to model these complex action distributions, allowing for probabilistic predictions that reflect the inherent uncertainty in forecasting future actions. These approaches move beyond simple categorization, instead aiming to understand how likely different actions are, ultimately providing a more nuanced and accurate representation of behavior.

Model Training: An Exercise in Controlled Hallucination

Model training is the iterative process of algorithmically learning patterns from datasets to create predictive models. This involves exposing the model to a training dataset, where it adjusts internal parameters to minimize the difference between its predictions and the actual observed values. The quality of the training data – its size, accuracy, and relevance – directly impacts model performance. Trained models then utilize these learned patterns to generate predictions on new, unseen data, effectively transforming raw data into actionable insights for forecasting, classification, or recommendation tasks. The process often involves validation and testing datasets to ensure the model generalizes well and avoids overfitting to the training data, further refining its predictive capabilities.

The creation of unbiased and generalizable predictive models relies fundamentally on data collection from a population that accurately reflects the characteristics of the group the model is intended to serve. Systemic biases present in non-representative samples can lead to skewed model outputs and inaccurate predictions when applied to the broader population. Specifically, under-representation of certain demographic groups, socioeconomic statuses, or behavioral patterns during data acquisition will result in models that perform poorly for those groups. Statistical techniques, such as stratified sampling and weighting, are often employed to mitigate these biases, but their effectiveness is directly linked to the initial representativeness of the collected data. A sufficiently large and diverse dataset is therefore crucial for ensuring model robustness and fairness across all relevant segments of the target population.

The prioritization of young individuals in initial behavioral model development stems from practical considerations regarding data procurement. Data collection efforts frequently target this demographic due to their higher rates of digital engagement and participation in research studies, resulting in larger and more readily accessible datasets. This accessibility simplifies the model training process and reduces associated costs. While models developed on young adult data may require subsequent refinement and validation across different age groups to ensure broader applicability, the initial focus on this population allows for faster prototyping and iterative improvement of predictive algorithms. This approach is not necessarily indicative of a long-term modeling strategy, but a pragmatic starting point given existing data landscapes.

Performance Across Demographics: The Fine Art of Post-hoc Rationalization

Evaluation of model performance specifically on older adult populations is crucial as this demographic often exhibits cognitive and perceptual differences not fully represented in typical training datasets. Discrepancies between model predictions and ground truth for older adults can indicate biases stemming from underrepresentation or insufficient data regarding age-related changes in behavior, sensory input, or cognitive processing. Performance deficits observed in this group may not reflect a general inadequacy of the model, but rather a limitation in its ability to generalize to individuals whose characteristics deviate from those prominently featured in the training data. Therefore, dedicated testing with older adults serves as a vital diagnostic step to identify and mitigate potential biases, ensuring equitable and reliable performance across all age groups.

Differences in behavioral distributions across age groups require the implementation of evaluation strategies specific to each demographic. Cognitive and physical changes associated with aging can manifest as variations in response times, error rates, and patterns of interaction with systems. Consequently, a single evaluation metric or dataset may not accurately reflect model performance across all age groups; for example, a metric optimized for young adults might underestimate the utility of a system for older adults. Tailored evaluation should involve the use of age-specific datasets, metrics that account for differing behavioral characteristics, and analyses that explicitly compare performance across groups to identify potential disparities and biases. This approach ensures a more comprehensive and reliable assessment of generalizability and facilitates the development of inclusive and equitable technologies.

Comprehensive model evaluation necessitates the use of diverse evaluation scenarios to determine prediction reliability across varied populations. This involves constructing test sets that represent the breadth of real-world conditions the model will encounter, including variations in environmental factors, input data quality, and user behavior. Specifically, scenarios should be designed to assess performance under both typical and edge-case conditions, and should include data from underrepresented demographic groups to identify potential biases or performance disparities. Failure to utilize a diverse range of evaluation scenarios can lead to overestimation of model accuracy and unreliable predictions when deployed in real-world settings, particularly for populations not adequately represented in the training or testing data.

The pursuit of seamless behavioral modeling, as demonstrated in this HAROOD benchmark, inevitably courts the reality of shifting distributions. The study meticulously details how models trained on young adults falter when applied to older populations – a predictable erosion of performance. It’s a cycle; each attempt to abstract human activity into neat datasets introduces further fragility. As Marvin Minsky observed, “You can’t always get what you want.” This paper isn’t about solving out-of-distribution generalization, but rather about quantifying its inevitability. The benchmark itself becomes a testament to the fact that production-in this case, the unpredictable behaviors of an aging population-will always find a way to break even the most elegant of theories. Documentation won’t save it; only a healthy dose of cynicism will.

What’s Next?

The creation of benchmarks like HAROOD feels…familiar. Another attempt to quantify a problem production systems solved years ago with generous error margins and a healthy dose of manual override. It is, predictably, a matter of distribution shift. The youthful vigor of training data rarely prepares a model for the nuanced declines of aging, or even the simple fact that people don’t consistently repeat themselves. The elegance of a generalized activity recognition model feels less impressive when one considers the endless edge cases, the unexpected gait changes, the simple decision to have a lie-in.

Future work will undoubtedly focus on domain adaptation techniques, adversarial training, and synthetic data generation. These are, of course, all variations on themes explored exhaustively in the past. The underlying issue isn’t a lack of algorithms, but the inherent messiness of human behavior. Attempts to model ‘normal’ aging will likely uncover just how much ‘normal’ varies, creating ever-more-granular sub-problems.

One suspects the ultimate solution won’t be a perfect algorithm, but a system that gracefully degrades, flags anomalies for human review, and accepts that predicting human activity with absolute certainty is a fool’s errand. Everything new is just the old thing with worse docs, and a more complicated dependency tree.

Original article: https://arxiv.org/pdf/2512.10807.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

Predicting the Predictable: The Illusion of Insight

Model Training: An Exercise in Controlled Hallucination

Performance Across Demographics: The Fine Art of Post-hoc Rationalization

What’s Next?

See also: